Registration is one of the key problems in medical image analysis. For mono-modal registration, it aims to recover a geometric transformation to align a pair of image data, which are different in some parts, so that the pair achieves the highest spatial correspondence. Many clinically important tasks, such as change analysis and data fusion, demand precise spatial alignment of such a data pair.
Traditional solutions for the mono-modal registration problem aim to find a domain mapping of specific type which minimizes overall mismatching errors. For rigid registration, such overall errors are of global nature, averaged over the entire domain. For non-rigid registration, the errors can be treated locally but some global regularization constraints are often exploited for making the problem tractable. These global factors enable to establish correspondences of the dissimilar data parts. At the same time, however, the global factors allow the dissimilar parts to influence overall registration accuracy even at similar parts. Moreover, in these solutions, specific choice of data and cost function dictate where the registration is accurate or inaccurate, disregarding any available clinical semantics and demands.